Meet the 2017 Amazon Fellows
Four graduate students from the Computing and Mathematical Sciences (CMS) Department and one from the Electrical Engineering (EE) Department have been selected as 2017 Amazon Fellows. This fellows program is the result of a partnership between Caltech and Amazon AWS around Machine Learning and Artificial Intelligence. The EE fellow is Srikanth Tenneti who is exploring the potential of deep learning for Direction of Arrival applications, and extending Ramanujan Sums based techniques for multi-dimensional periodicity extraction. CMS graduate student Navid Azizan Ruhi is researching faster optimization algorithms for machine learning. He is looking forward to visiting Amazon AI as a fellow and exchanging ideas with their researchers. Computer science graduate student Hoang Le is developing methods for efficient and intelligent sequential decision making in realistic systems. Florian Schaefer, whose focus is applied and computational mathematics, is researching the interface of statistical estimation and the design of fast algorithms. Control and dynamical systems graduate student Ellen Feldman, working with Professor Joel Burdick, has used part of the funding to present her research at the Society for Neuroscience annual meeting and looking forward to other future opportunities to share her research.
P. P. Vaidyanathan
Navid Azizan Ruhi
Progress for Paraplegics
Joel W. Burdick, Richard L. and Dorothy M. Hayman Professor of Mechanical Engineering and Bioengineering, and Yu-Chong Tai, Professor of Electrical Engineering and Mechanical Engineering, are developing new technologies to expand their research which has enabled a paraplegic man to stand and move his legs voluntarily. The team has until now used intelligent guesswork to determine which stimuli might work best. But soon, using a new algorithm developed by Professor Burdick, they will be able to rely on a computer to determine the optimum stimulation levels, based on the patient's response to previous stimuli. This would allow patients to go home after the extensive rehab process with a system that could be continually adjusted by computer. [Caltech Release] [ENGenious Progress Report]